Modeling and simulation of non-Darcy or turbulent flow are well documented in the literature and available in commercial reservoir simulators (E300, Intersect) only for gas wells rather than oil wells. There is a need to model non-Darcy or turbulent flow in reservoir simulation for oil wells in the carbonate reservoirs with highly connected and densely distributed fractures and karst. This paper proposes a new non-Darcy or turbulent flow modeling and simulation method for oil wells. Unlike the industry's existing methods for non-Darcy or turbulent flow that focus on the non-Darcy coefficient only, this paper presents a new method that models the ratio between non-Darcy and Darcy flows such that a unified model for a field or a region can be created, which significantly simplifies the non-Darcy or turbulent flow modeling process for multiple wells, especially for future wells. The ratio-based method is simple and comprehensive. It can be easily calibrated with MRT (multiple-rate test) data and implemented into in-house or commercial reservoir simulators using a simulator supported scripting language, e.g., Python etc. Kashagan is the world's largest oil reservoir discovered in the last 30 years that contains highly connected and densely distributed fractures and karst in its rim. The oil production rate for a well in the rim can be higher than several tens KSTB/D if it is not constrained by the facility. The current MRT data in all tested wells clearly show non-Darcy flow phenomenon and confirm that modeling non-Darcy flow is necessary to the field. Kashagan had experienced difficulties to match BHP (bottom hole pressure) and large errors in the blind test due to the OPEC's production curtailment and high-rate tests. Build-up pressure curves were miss-matched and HM (history match) of the crossflows (10 KSTB/D with less than 10 psi) in the bottomhole of a PLT (production logging tool) well during shut-in was challenging. Since modeling non-Darcy flow for oil wells in the commercial simulators, e.g., E300 and Intersect, is unavailable, the simulation team in NCOC has created a new method for the needs of non-Darcy modeling and simulation. The applications of the new method have resulted in the excellent results and solved the issues of history matching BHP, high/low-rate tests, build-up pressure trends, and bottomhole crossflows.

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In order to run reservoir simulation efficiently, a coarse scale (CS) dynamic model is created by upscaling of a fine scale (FS) static model. All history match (HM) changes usually done in the CS dynamic model need to be downscaled to FS for geological justifications and consistency maintenance between the FS static and CS dynamic models. This paper proposes a robust downscaling method for integration of FS static and CS dynamic models. The proposed method downscales a HMDM (dynamic model) to HMSM (static) in multiple steps. Scale-up the ISM (initial) to CS to create an IDM. Identify the cell changes between HMDM and IDM, and transfer the changes to FS to create a MSM (modified). Scale-up the MSM to CS to create to a MDM and calculate the ratios between HMDM and MDM for all cell properties. Transfer the ratios to FS to create a HMSM. Scale-up the HMSM to CS to confirm its identity to the HMDM. Selection of sampling and zone mapping methods is critical in all steps. The proposed method has been successfully applied in a giant carbonate oil field in the Caspian Sea that consists of a matrix dominated platform and a fracture/karst dominated rim. Due to the field's complex geology and high H2S content (15%), a dual porosity, dual permeability compositional model has been created to model compositional sour crude flow within/between matrix and fracture/karst. The FS static model contains a 236m × 236m horizontal grid with 593 layers while the CS dynamic model has the horizontal cell sizes in a range of 236m to 944m with 73 layers. Rock regions, permeability, and reservoir connectivity in the CS dynamic model were calibrated using the field historical production data (e.g., static pressure, PLT, interference test, and GOR/water-cut data) to create a HMDM. Since the HM process was performed only in the CS dynamic model, the FS static model and HMDM became inconsistent. Appling the proposed downscaling method has helped the HM team to resolve this issue and resulted in a seamless link between the FS static and CS dynamic models for current and future HM and model updates.

Running a fine grid model with 107 - 109 of cells is possible using a supercomputer with 103 - 106 of CPUs but may not be always cost-effective. The most cost-effective way is to use a coarse grid model that is much smaller but with static/dynamic profiles very close to the fine grid model. This paper proposes a new layer optimization and upscaling method with the aim for creating a consistent coarse grid model. Unlike the industry's existing layer optimization and upscaling methods, the proposed method performs layer optimization and upscaling fully integrated with the Lorenz coefficient and curves (LCC). Coarse grid layers and their permeabilities are created by minimizing the difference between fine and coarse grid LCCs. The process consists of static and dynamic optimizations. The former is measured by LCC while the latter by pressure, GOR, and water-cut. A new LCC-based permeability upscaling method is developed to preserve the fine grid multiphase flow behaviors. A satisfactory coarse grid model is achieved when both static and dynamic criteria are met. The proposed method has been successfully applied to a giant carbonate oil field in the Caspian Sea that consists of a matrix dominated platform and a fracture/karst dominated rim. Due to the field's complex geology and high H2S content (15%), a dual porosity, dual permeability compositional model has been created to model compositional sour crude flow within and between the matrix and fracture/karst features. The reservoir drive mechanisms are fluid expansion, miscible gas injection and aquifer drive. The reservoir is undersaturated and has an abnormally high initial reservoir pressure. The fine-grid static model contains 104 million cells (370×225×625×2) and the optimized upscaled coarse-grid dynamic model has 8.3 million cells (370×225×50×2). The upscaled model can be run efficiently on the company's existing HPC infrastructure with a maximum of 64 CPUs. Excellent matches of the Lorenz coefficient maps for reservoir total/zones and Lorenz curves at all wells between the fine and coarse grid models have been achieved. Matches on the dynamic variables, e.g., pressure, gas breakthrough time, and GOR growth, in all producers are within the defined acceptable tolerances. The high quality of the static and dynamic matches between the coarse- and fine-grid models confirms that the reservoir properties of the coarse-grid model is very close to the fine-grid model and can be used a base model for history matching and uncertainty analysis.